SPAML Item Data Codebook

Set Options

knitr::opts_chunk$set(
  warning = TRUE, # show warnings during codebook generation
  message = TRUE, # show messages during codebook generation
  error = TRUE, # do not interrupt codebook generation in case of errors,
    # usually better for debugging
  echo = TRUE  # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())

library(rio)
library(labelled)

Prep Data

library(codebook)

## 
## Attaching package: 'codebook'

## The following object is masked from 'package:labelled':
## 
##     to_factor

codebook_data <- import("../data_processing/output_data/item_data/sr_item_data.csv")

var_label(codebook_data) <- list(
  word = "Original stimulus showed to the participant in the language of the study.",
  class = "Word/nonword trial type indicator, which tells you what the correct answer is for the trial.",
  avgRT = "Average response latency for the stimulus item across all participants.",
  avgZ_RT = "Average z-scored response latency for the stimulus item - z-scored by participant session, then averaged across the item, regardless of condition.",
  samplesize = "The number of participants who were shown that item.",
  n_answered = "The number of participants who answered that item (i.e., it did not time out.",
  seRT = "The standard error of the raw response latency.",
  seZ_RT = "The standard error of the z-scored response latency.",
  accuracy = "The proportion of correct answers out of total answered for that item.",
  Z2.5_avgRT = "The average raw response latency excluding outliers of 2.5 Z or higher.",
  Z2.5_avgZ_RT = "The average Z-scored response latency excluding outliers of 2.5 Z or higher.",
  Z2.5_samplesize = "The number of data points viewed that were not Z equal 2.5 or higher", 
  Z2.5_n_answered = "The number of data points viewed that were not Z equal 2.5 or higher. Note that viewed and answered are the same because you had to answer the item (correctly) for it be to included in the Z-score calculation.",
  Z2.5_seRT = "Standard error of the raw response latency after removing Z equal 2.5 or higher.",
  Z2.5_seZ_RT = "Standard error of the Z-scored response latency after removing Z equal 2.5 or higher.",
  Z2.5_accuracy = "The accuracy for Z scored response latencies after excluding Z greater than 2.5 and higher. Note that you had to get the item right for it to be included in the Z-score calculation, so this value is always 1.", 
  Z3.0_avgRT = "The average raw response latency excluding outliers of 3.0 Z or higher.",
  Z3.0_avgZ_RT = "The average Z-scored response latency excluding outliers of 3.0 Z or higher.",
  Z3.0_samplesize = "The number of data points viewed that were not Z equal 3.0 or higher", 
  Z3.0_n_answered = "The number of data points viewed that were not Z equal 3.0 or higher. Note that viewed and answered are the same because you had to answer the item (correctly) for it be to included in the Z-score calculation.",
  Z3.0_seRT = "Standard error of the raw response latency after removing Z equal 3.0 or higher.",
  Z3.0_seZ_RT = "Standard error of the Z-scored response latency after removing Z equal 3.0 or higher.",
  Z3.0_accuracy = "The accuracy for Z scored response latencies after excluding Z greater than 3.0 and higher. Note that you had to get the item right for it to be included in the Z-score calculation, so this value is always 1.")

metadata(codebook_data)$name <- "Semantic Priming Across Many Languages (Example using Serbian data)"
metadata(codebook_data)$description <- "This dataset includes the summarized item data from the SPAML project (example is specifically Serbian, but all files are structured the same way). The data is averaged over items, regardless of condition for words (i.e., related or unrelated trial). 

Semantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks. These studies provide insight into the cognitive underpinnings of semantic representations in both healthy and clinical populations; however, they have suffered from several issues including generally low sample sizes and a lack of diversity in linguistic implementations. Here, we will test the size and the variability of the semantic priming effect across ten languages by creating a large database of semantic priming values, based on an adaptive sampling procedure. Differences in response latencies between related word-pair conditions and unrelated word-pair conditions (i.e., difference score confidence interval is greater than zero) will allow quantifying evidence for semantic priming, whereas improvements in model fit with the addition of a random intercept for language will provide support for variability in semantic priming across languages."
metadata(codebook_data)$identifier <- "https://doi.org/10.5281/zenodo.10888833"
metadata(codebook_data)$creator <- "Erin M. Buchanan"
metadata(codebook_data)$citation <- "Buchanan, E., Cuccolo, K., Heyman, T., Iyer, A., Coles, N., Lewis Jr, N., Peters, K., van Berkel, N., Taylor, J., Van't Veer, A. E., Montefinese, M., Valentine, K. D., Maxwell, N., Türkan, B. N., Williams, G., Oliveros-Chacana, J. C., Röer, J., Fini, C., Acar, O., … Lewis, S. C. (2024). SemanticPriming/SPAML: SPAML v1 Data Release (v1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10888833"
metadata(codebook_data)$url <- "https://github.com/SemanticPriming/SPAML/releases/"
metadata(codebook_data)$datePublished <- "2024-05-01"
metadata(codebook_data)$temporalCoverage <- "2022-2024" 
metadata(codebook_data)$spatialCoverage <- "Online" 

Create codebook

codebook(codebook_data)

Metadata

Description

Dataset name: Semantic Priming Across Many Languages (Example using Serbian data)

This dataset includes the summarized item data from the SPAML project (example is specifically Serbian, but all files are structured the same way). The data is averaged over items, regardless of condition for words (i.e., related or unrelated trial).

Semantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks. These studies provide insight into the cognitive underpinnings of semantic representations in both healthy and clinical populations; however, they have suffered from several issues including generally low sample sizes and a lack of diversity in linguistic implementations. Here, we will test the size and the variability of the semantic priming effect across ten languages by creating a large database of semantic priming values, based on an adaptive sampling procedure. Differences in response latencies between related word-pair conditions and unrelated word-pair conditions (i.e., difference score confidence interval is greater than zero) will allow quantifying evidence for semantic priming, whereas improvements in model fit with the addition of a random intercept for language will provide support for variability in semantic priming across languages.

Metadata for search engines

• Temporal Coverage: 2022-2024

• Spatial Coverage: Online

• Citation: Buchanan, E., Cuccolo, K., Heyman, T., Iyer, A., Coles, N., Lewis Jr, N., Peters, K., van Berkel, N., Taylor, J., Van’t Veer, A. E., Montefinese, M., Valentine, K. D., Maxwell, N., Türkan, B. N., Williams, G., Oliveros-Chacana, J. C., Röer, J., Fini, C., Acar, O., … Lewis, S. C. (2024). SemanticPriming/SPAML: SPAML v1 Data Release (v1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10888833

• URL: https://github.com/SemanticPriming/SPAML/releases/

• Identifier: https://doi.org/10.5281/zenodo.10888833

• Date published: 2024-05-01

• Creator:

name	value
1	Erin M. Buchanan

x word class avgRT avgZ_RT samplesize n_answered seRT seZ_RT accuracy Z2.5_avgRT Z2.5_avgZ_RT Z2.5_samplesize Z2.5_n_answered Z2.5_seRT Z2.5_seZ_RT Z2.5_accuracy Z3.0_avgRT Z3.0_avgZ_RT Z3.0_samplesize Z3.0_n_answered Z3.0_seRT Z3.0_seZ_RT Z3.0_accuracy

#Variables

word

Original stimulus showed to the participant in the language of the study.

Distribution

Distribution of values for word

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
word	Original stimulus showed to the participant in the language of the study.	character	0	1	3917	0	2	20	0

class

Word/nonword trial type indicator, which tells you what the correct answer is for the trial.

Distribution

Distribution of values for class

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
class	Word/nonword trial type indicator, which tells you what the correct answer is for the trial.	character	0	1	2	0	4	7	0

avgRT

Average response latency for the stimulus item across all participants.

Distribution

Distribution of values for avgRT

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
avgRT	Average response latency for the stimulus item across all participants.	numeric	0	1	383	854	1756	892.9244	194.4628	▁▇▃▁▁

avgZ_RT

Average z-scored response latency for the stimulus item - z-scored by participant session, then averaged across the item, regardless of condition.

Distribution

Distribution of values for avgZ_RT

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
avgZ_RT	Average z-scored response latency for the stimulus item - z-scored by participant session, then averaged across the item, regardless of condition.	numeric	0	1	-1.4	-0.083	2.8	0.0194038	0.5490782	▂▇▃▁▁

samplesize

The number of participants who were shown that item.

Distribution

Distribution of values for samplesize

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
samplesize	The number of participants who were shown that item.	numeric	0	1	1	124	366	125.7955	24.74073	▁▇▁▁▁

n_answered

The number of participants who answered that item (i.e., it did not time out.

Distribution

Distribution of values for n_answered

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
n_answered	The number of participants who answered that item (i.e., it did not time out.	numeric	0	1	72	130	412	132.8486	23.89708	▇▂▁▁▁

seRT

The standard error of the raw response latency.

Distribution

Distribution of values for seRT

1 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
seRT	The standard error of the raw response latency.	numeric	1	0.9997447	0.84	30	143	31.91637	10.79077	▇▇▁▁▁

seZ_RT

The standard error of the z-scored response latency.

Distribution

Distribution of values for seZ_RT

1 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
seZ_RT	The standard error of the z-scored response latency.	numeric	1	0.9997447	0.03	0.07	0.47	0.0747394	0.0258709	▇▁▁▁▁

accuracy

The proportion of correct answers out of total answered for that item.

Distribution

Distribution of values for accuracy

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
accuracy	The proportion of correct answers out of total answered for that item.	numeric	0	1	0.0092	0.97	1	0.9470538	0.0771837	▁▁▁▁▇

Z2.5_avgRT

The average raw response latency excluding outliers of 2.5 Z or higher.

Distribution

Distribution of values for Z2.5_avgRT

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
Z2.5_avgRT	The average raw response latency excluding outliers of 2.5 Z or higher.	numeric	0	1	383	838	1391	863.0617	164.703	▁▇▇▃▁

Z2.5_avgZ_RT

The average Z-scored response latency excluding outliers of 2.5 Z or higher.

Distribution

Distribution of values for Z2.5_avgZ_RT

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
Z2.5_avgZ_RT	The average Z-scored response latency excluding outliers of 2.5 Z or higher.	numeric	0	1	-1.4	-0.13	1.4	-0.0748136	0.4520479	▁▇▇▃▁

Z2.5_samplesize

The number of data points viewed that were not Z equal 2.5 or higher

Distribution

Distribution of values for Z2.5_samplesize

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
Z2.5_samplesize	The number of data points viewed that were not Z equal 2.5 or higher	numeric	0	1	1	121	363	122.1008	25.37061	▁▇▁▁▁

Z2.5_n_answered

The number of data points viewed that were not Z equal 2.5 or higher. Note that viewed and answered are the same because you had to answer the item (correctly) for it be to included in the Z-score calculation.

Distribution

Distribution of values for Z2.5_n_answered

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
Z2.5_n_answered	The number of data points viewed that were not Z equal 2.5 or higher. Note that viewed and answered are the same because you had to answer the item (correctly) for it be to included in the Z-score calculation.	numeric	0	1	1	121	363	122.1008	25.37061	▁▇▁▁▁

Z2.5_seRT

Standard error of the raw response latency after removing Z equal 2.5 or higher.

Distribution

Distribution of values for Z2.5_seRT

1 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
Z2.5_seRT	Standard error of the raw response latency after removing Z equal 2.5 or higher.	numeric	1	0.9997447	0.84	27	143	28.97971	9.645963	▇▆▁▁▁

Z2.5_seZ_RT

Standard error of the Z-scored response latency after removing Z equal 2.5 or higher.

Distribution

Distribution of values for Z2.5_seZ_RT

1 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
Z2.5_seZ_RT	Standard error of the Z-scored response latency after removing Z equal 2.5 or higher.	numeric	1	0.9997447	0.028	0.058	0.29	0.0607403	0.0161149	▇▁▁▁▁

Z2.5_accuracy

The accuracy for Z scored response latencies after excluding Z greater than 2.5 and higher. Note that you had to get the item right for it to be included in the Z-score calculation, so this value is always 1.

Distribution

Distribution of values for Z2.5_accuracy

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
Z2.5_accuracy	The accuracy for Z scored response latencies after excluding Z greater than 2.5 and higher. Note that you had to get the item right for it to be included in the Z-score calculation, so this value is always 1.	numeric	0	1	1	1	1	1	0	▁▁▇▁▁

Z3.0_avgRT

The average raw response latency excluding outliers of 3.0 Z or higher.

Distribution

Distribution of values for Z3.0_avgRT

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
Z3.0_avgRT	The average raw response latency excluding outliers of 3.0 Z or higher.	numeric	0	1	383	844	1455	873.9104	175.4686	▁▇▇▃▁

Z3.0_avgZ_RT

The average Z-scored response latency excluding outliers of 3.0 Z or higher.

Distribution

Distribution of values for Z3.0_avgZ_RT

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
Z3.0_avgZ_RT	The average Z-scored response latency excluding outliers of 3.0 Z or higher.	numeric	0	1	-1.4	-0.12	1.7	-0.0449096	0.4814571	▁▇▆▂▁

Z3.0_samplesize

The number of data points viewed that were not Z equal 3.0 or higher

Distribution

Distribution of values for Z3.0_samplesize

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
Z3.0_samplesize	The number of data points viewed that were not Z equal 3.0 or higher	numeric	0	1	1	123	364	123.5933	25.04458	▁▇▁▁▁

Z3.0_n_answered

The number of data points viewed that were not Z equal 3.0 or higher. Note that viewed and answered are the same because you had to answer the item (correctly) for it be to included in the Z-score calculation.

Distribution

Distribution of values for Z3.0_n_answered

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
Z3.0_n_answered	The number of data points viewed that were not Z equal 3.0 or higher. Note that viewed and answered are the same because you had to answer the item (correctly) for it be to included in the Z-score calculation.	numeric	0	1	1	123	364	123.5933	25.04458	▁▇▁▁▁

Z3.0_seRT

Standard error of the raw response latency after removing Z equal 3.0 or higher.

Distribution

Distribution of values for Z3.0_seRT

1 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
Z3.0_seRT	Standard error of the raw response latency after removing Z equal 3.0 or higher.	numeric	1	0.9997447	0.84	28	143	29.9302	10.00673	▇▆▁▁▁

Z3.0_seZ_RT

Standard error of the Z-scored response latency after removing Z equal 3.0 or higher.

Distribution

Distribution of values for Z3.0_seZ_RT

1 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
Z3.0_seZ_RT	Standard error of the Z-scored response latency after removing Z equal 3.0 or higher.	numeric	1	0.9997447	0.028	0.061	0.35	0.0640459	0.0178888	▇▁▁▁▁

Z3.0_accuracy

The accuracy for Z scored response latencies after excluding Z greater than 3.0 and higher. Note that you had to get the item right for it to be included in the Z-score calculation, so this value is always 1.

Distribution

Distribution of values for Z3.0_accuracy

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
Z3.0_accuracy	The accuracy for Z scored response latencies after excluding Z greater than 3.0 and higher. Note that you had to get the item right for it to be included in the Z-score calculation, so this value is always 1.	numeric	0	1	1	1	1	1	0	▁▁▇▁▁

Missingness report

Codebook table

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "Semantic Priming Across Many Languages (Example using Serbian data)",
  "description": "This dataset includes the summarized item data from the SPAML project (example is specifically Serbian, but all files are structured the same way). The data is averaged over items, regardless of condition for words (i.e., related or unrelated trial). \n\nSemantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks. These studies provide insight into the cognitive underpinnings of semantic representations in both healthy and clinical populations; however, they have suffered from several issues including generally low sample sizes and a lack of diversity in linguistic implementations. Here, we will test the size and the variability of the semantic priming effect across ten languages by creating a large database of semantic priming values, based on an adaptive sampling procedure. Differences in response latencies between related word-pair conditions and unrelated word-pair conditions (i.e., difference score confidence interval is greater than zero) will allow quantifying evidence for semantic priming, whereas improvements in model fit with the addition of a random intercept for language will provide support for variability in semantic priming across languages.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n[truncated]\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "identifier": "https://doi.org/10.5281/zenodo.10888833",
  "creator": "Erin M. Buchanan",
  "citation": "Buchanan, E., Cuccolo, K., Heyman, T., Iyer, A., Coles, N., Lewis Jr, N., Peters, K., van Berkel, N., Taylor, J., Van't Veer, A. E., Montefinese, M., Valentine, K. D., Maxwell, N., Türkan, B. N., Williams, G., Oliveros-Chacana, J. C., Röer, J., Fini, C., Acar, O., … Lewis, S. C. (2024). SemanticPriming/SPAML: SPAML v1 Data Release (v1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10888833",
  "url": "https://github.com/SemanticPriming/SPAML/releases/",
  "datePublished": "2024-05-01",
  "temporalCoverage": "2022-2024",
  "spatialCoverage": "Online",
  "keywords": ["word", "class", "avgRT", "avgZ_RT", "samplesize", "n_answered", "seRT", "seZ_RT", "accuracy", "Z2.5_avgRT", "Z2.5_avgZ_RT", "Z2.5_samplesize", "Z2.5_n_answered", "Z2.5_seRT", "Z2.5_seZ_RT", "Z2.5_accuracy", "Z3.0_avgRT", "Z3.0_avgZ_RT", "Z3.0_samplesize", "Z3.0_n_answered", "Z3.0_seRT", "Z3.0_seZ_RT", "Z3.0_accuracy"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "word",
      "description": "Original stimulus showed to the participant in the language of the study.",
      "@type": "propertyValue"
    },
    {
      "name": "class",
      "description": "Word/nonword trial type indicator, which tells you what the correct answer is for the trial.",
      "@type": "propertyValue"
    },
    {
      "name": "avgRT",
      "description": "Average response latency for the stimulus item across all participants.",
      "@type": "propertyValue"
    },
    {
      "name": "avgZ_RT",
      "description": "Average z-scored response latency for the stimulus item - z-scored by participant session, then averaged across the item, regardless of condition.",
      "@type": "propertyValue"
    },
    {
      "name": "samplesize",
      "description": "The number of participants who were shown that item.",
      "@type": "propertyValue"
    },
    {
      "name": "n_answered",
      "description": "The number of participants who answered that item (i.e., it did not time out.",
      "@type": "propertyValue"
    },
    {
      "name": "seRT",
      "description": "The standard error of the raw response latency.",
      "@type": "propertyValue"
    },
    {
      "name": "seZ_RT",
      "description": "The standard error of the z-scored response latency.",
      "@type": "propertyValue"
    },
    {
      "name": "accuracy",
      "description": "The proportion of correct answers out of total answered for that item.",
      "@type": "propertyValue"
    },
    {
      "name": "Z2.5_avgRT",
      "description": "The average raw response latency excluding outliers of 2.5 Z or higher.",
      "@type": "propertyValue"
    },
    {
      "name": "Z2.5_avgZ_RT",
      "description": "The average Z-scored response latency excluding outliers of 2.5 Z or higher.",
      "@type": "propertyValue"
    },
    {
      "name": "Z2.5_samplesize",
      "description": "The number of data points viewed that were not Z equal 2.5 or higher",
      "@type": "propertyValue"
    },
    {
      "name": "Z2.5_n_answered",
      "description": "The number of data points viewed that were not Z equal 2.5 or higher. Note that viewed and answered are the same because you had to answer the item (correctly) for it be to included in the Z-score calculation.",
      "@type": "propertyValue"
    },
    {
      "name": "Z2.5_seRT",
      "description": "Standard error of the raw response latency after removing Z equal 2.5 or higher.",
      "@type": "propertyValue"
    },
    {
      "name": "Z2.5_seZ_RT",
      "description": "Standard error of the Z-scored response latency after removing Z equal 2.5 or higher.",
      "@type": "propertyValue"
    },
    {
      "name": "Z2.5_accuracy",
      "description": "The accuracy for Z scored response latencies after excluding Z greater than 2.5 and higher. Note that you had to get the item right for it to be included in the Z-score calculation, so this value is always 1.",
      "@type": "propertyValue"
    },
    {
      "name": "Z3.0_avgRT",
      "description": "The average raw response latency excluding outliers of 3.0 Z or higher.",
      "@type": "propertyValue"
    },
    {
      "name": "Z3.0_avgZ_RT",
      "description": "The average Z-scored response latency excluding outliers of 3.0 Z or higher.",
      "@type": "propertyValue"
    },
    {
      "name": "Z3.0_samplesize",
      "description": "The number of data points viewed that were not Z equal 3.0 or higher",
      "@type": "propertyValue"
    },
    {
      "name": "Z3.0_n_answered",
      "description": "The number of data points viewed that were not Z equal 3.0 or higher. Note that viewed and answered are the same because you had to answer the item (correctly) for it be to included in the Z-score calculation.",
      "@type": "propertyValue"
    },
    {
      "name": "Z3.0_seRT",
      "description": "Standard error of the raw response latency after removing Z equal 3.0 or higher.",
      "@type": "propertyValue"
    },
    {
      "name": "Z3.0_seZ_RT",
      "description": "Standard error of the Z-scored response latency after removing Z equal 3.0 or higher.",
      "@type": "propertyValue"
    },
    {
      "name": "Z3.0_accuracy",
      "description": "The accuracy for Z scored response latencies after excluding Z greater than 3.0 and higher. Note that you had to get the item right for it to be included in the Z-score calculation, so this value is always 1.",
      "@type": "propertyValue"
    }
  ]
}`